Prediction of future paths of mobile objects using path library

In situational awareness, the ability to make predictions about the near future situation in the area under surveillance is often as essential as being aware of the current situation. We introduce a privacy-preser-ving instance-based prediction method, where a path library is collected by learning earlier paths of mobile objects in the area of surveillance. The input to the pre-diction is the most recent coordinates of the objects in the scene. Based on similarity to short segments of currently tracked paths, a relative weight is associated with each path in the library. Future paths are predicted by computing the weighted average of the library paths. We demonstrate the operation of a situational awareness system where privacy-preserving data are extracted from an inexpensive computer vision which consists of a camera-equipped Raspberry PI -based edge device. The system runs a deep neural network-based object detection algorithm on the camera feed and stores the coordinates, object class labels, and timestamps of the detected objects. We used probabilistic reasoning based on joint probabilistic data association, Hungarian algorithm, and Kalman filter to infer which detections from different time instances came from the same object.